Election monitoring increasingly depends on the capacity to process high-volume citizen reports, social media posts, and platform-based submissions under conditions of uncertainty. This study evaluates whether a zero-shot large language model (LLM) pipeline can function as a high-recall triage system for election monitoring using VoteReportPH data from the 2025 Philippine elections. Drawing on signal detection theory, information overload theory, and human-AI complementarity, the study frames LLM classification as decision support rather than autonomous adjudication. The analysis used a postprocessed Election Monitoring System dataset of 3,618 reports and a cleaned model-evaluation dataset of 4,158 reports. For binary validity detection, the model correctly surfaced 166 of 181 human-validated reports, yielding a recall of 0.9171, specificity of 0.8973, and accuracy of 0.8983, but low precision of 0.3198. This error profile indicates a recall-oriented filter that reduces missed incidents while forwarding false positives for human review. In multiclass incident categorization, the model performed strongly on explicit categories such as automated counting machine errors and illegal campaigning, but weakly on rare, residual, and procedurally ambiguous categories. The findings show that zero-shot LLMs can support civic monitoring as triage infrastructure, but they require human verification, transparent error handling, and category-specific workflow design.

Curso IA Ejercicio Final

Curso IA EjercicioFinal

El presente código y sus resultados son el análisis del caso 1 del curso de Simulación